Accessibility: a performance measure for land-use and transportation planning in the Montréal Metropolitan Region
Bibliographic record
Abstract
Accessibility is a comprehensive performance measure of the interaction between the land-use and transportation systems. In this research project, a variety of location-based and individual accessibility measures are described and applied to the Montréal Metropolitan Region for the first time. Accessibility to jobs, workers and retail is measured using location-based accessibility measures, some including competition factors and based on commute-flow data. The results illustrate the complex relationships between Montréal’s employment centers and residential neighborhoods and help understand the influence of Montréal’s major transportation infrastructures, which are the highway network and the metro and commuter rail systems. Accessibility measures are useful as complements, and eventually as alternatives to traditional mobility measures. Accessibility is valued by individuals and has an impact on home sale values and household travel behavior. A hedonic regression shows that in the Montréal region a premium is paid for increased levels of regional accessibility to jobs and retail. An analysis of household activity spaces establishes a relationship between high levels of regional accessibility and shorter, smaller and more local travel patterns. This study provides planners and decision makers with a wealth of information on accessibility in Montréal and an explanation of a variety of measures of accessibility as well as a demonstration of their application to plan making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".